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GTOC 9: Results from Michigan Technological Univeristy and University of Michigan (MTU-UoM)

机译:GTOC 9:密歇根技术大学和密西根大学(MTU-UoM)的结果

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This paper presents the methods developed by the Michigan Tech Univeristy and University of Michigan (MTU-UoM) team in the 9th GTOC along with the obtained results. Several concepts were investigated, specially regarding the selection of the sequence of debris to be removed in each mission. These concepts will be briefed in this paper and the concept that produced this team’s best solution is presented in detail. A genetic algorithm is used as an outer loop optimization tool to determine the sequence of debris to be removed. An inner loop optimizer is used to tune the individual transfers in each mission. This team’s best solution consists of 16 missions that removes 122 debris with a cost of 1192.74 MEURs.
机译:本文介绍了由密歇根理工大学和密歇根大学(MTU-UoM)团队在第9届GTOC上开发的方法以及获得的结果。研究了几种概念,特别是在每次任务中选择要清除的碎片顺序方面。这些概念将在本文中进行简要介绍,并详细介绍产生该团队最佳解决方案的概念。遗传算法用作外循环优化工具,以确定要清除的碎片的顺序。内部循环优化器用于调整每个任务中的各个传输。该团队的最佳解决方案包括执行16次任务,清除122块杂物,成本为1192.74 MEUR。

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